用 Kandy 实现 Kotlin Notebook 中数据可视化

Kotlin offers an all-in-one-place solution for powerful and flexible data visualization, providing an intuitive way to present and explore data before diving into complex models.

This tutorial demonstrates how to create different chart types in IntelliJ IDEA using Kotlin Notebook with the Kandy and Kotlin DataFrame libraries.

Before you start

  1. Download and install the latest version of IntelliJ IDEA Ultimate.

  2. Install the Kotlin Notebook plugin in IntelliJ IDEA.

    Alternatively, access the Kotlin Notebook plugin from Settings | Plugins | Marketplace within IntelliJ IDEA.

  3. Create a new notebook by selecting File | New | Kotlin Notebook.

  4. In your notebook, import the Kandy and Kotlin DataFrame libraries by running the following command:

     %use kandy
 %use dataframe

Create the DataFrame

Start by creating the DataFrame containing the records to visualize. This DataFrame stores simulated numbers of the monthly average temperature in three cities: Berlin, Madrid, and Caracas.

Use the dataFrameOf() function from the Kotlin DataFrame library to generate the DataFrame. Run the following code snippet in Kotlin Notebook:

// The months variable stores a list with the 12 months of the year
val months = listOf(
    "January", "February",
    "March", "April", "May",
    "June", "July", "August",
    "September", "October", "November",
    "December"
)
// The tempBerlin, tempMadrid, and tempCaracas variables store a list with temperature values for each month
val tempBerlin =
    listOf(-0.5, 0.0, 4.8, 9.0, 14.3, 17.5, 19.2, 18.9, 14.5, 9.7, 4.7, 1.0)
val tempMadrid =
    listOf(6.3, 7.9, 11.2, 12.9, 16.7, 21.1, 24.7, 24.2, 20.3, 15.4, 9.9, 6.6)
val tempCaracas =
    listOf(27.5, 28.9, 29.6, 30.9, 31.7, 35.1, 33.8, 32.2, 31.3, 29.4, 28.9, 27.6)

// The df variable stores a DataFrame of three columns, including records of months, temperature, and cities
val df = dataFrameOf(
    "Month" to months + months + months,
    "Temperature" to tempBerlin + tempMadrid + tempCaracas,
    "City" to List(12) { "Berlin" } + List(12) { "Madrid" } + List(12) { "Caracas" }
)

Explore the structure of the new DataFrame by looking into the first four rows:

df.head(4)

You can see that the DataFrame has three columns: Month, Temperature, and City. The first four rows of the DataFrame contain records of the temperature in Berlin from January to April:

Dataframe exploration

There are different options to access a column's records that can help you increase type safety when working with the Kandy and Kotlin DataFrame libraries together. For more information, see Access APIs.

Create a line chart

Let's create a line chart in Kotlin Notebook using the df DataFrame from the previous section.

Use the plot() function from the Kandy library. Within the plot() function, specify the type of chart (in this case, it's line) and the values for the X and Y axes. You can customize colors and sizes:

df.plot {
    line {
        // Accesses the DataFrame's columns used for the X and Y axes 
        x(Month)
        y(Temperature)
        // Accesses the DataFrame's column used for categories and sets colors for these categories 
        color(City) {
            scale = categorical("Berlin" to Color.PURPLE, "Madrid" to Color.ORANGE, "Caracas" to Color.GREEN)
        }
        // Customizes the line's size
        width = 1.5
    }
    // Customizes the chart's layout size
    layout.size = 1000 to 450
}

Here's the result:

Line chart

Create a points chart

Now, let's visualize the df DataFrame in a points (scatter) chart.

Within the plot() function, specify the points chart type. Add the X and Y axes' values and the categorical values from the df columns. You can also include a heading to your chart:

df.plot {
    points {
        // Accesses the DataFrame's columns used for the X and Y axes 
        x(Month) { axis.name = "Month" }
        y(Temperature) { axis.name = "Temperature" }
        // Customizes the point's size
        size = 5.5
        // Accesses the DataFrame's column used for categories and sets colors for these categories 
        color(City) {
            scale = categorical("Berlin" to Color.LIGHT_GREEN, "Madrid" to Color.BLACK, "Caracas" to Color.YELLOW)
        }
    }
    // Adds a chart heading
    layout.title = "Temperature per month"
}

Here's the result:

Points chart

Create a bar chart

Finally, let's create a bar chart grouped by city using the same data as in the previous charts. For colors, you can also use hexadecimal codes: 

// Groups by cities  
df.groupBy { City }.plot {
    // Adds a chart heading
    layout.title = "Temperature per month"
    bars {
        // Accesses the DataFrame's columns used for the X and Y axes 
        x(Month)
        y(Temperature)
        // Accesses the DataFrame's column used for categories and sets colors for these categories 
        fillColor(City) {
            scale = categorical(
                "Berlin" to Color.hex("#6F4E37"),
                "Madrid" to Color.hex("#C2D4AB"),
                "Caracas" to Color.hex("#B5651D")
            )
        }
    }
}

Here's the result:

Bar chart

What's next